Transactions on Energy Systems and Engineering Applications

Closed loop battery current controlled zeta converter for improved power quality in electric vehicle charging stations

2024-06-11T16:08:22+00:00

To encourage an eco-friendly environment and pollution-free transportation, most of the automobile industries are promoting electric vehicles. However, with the adoption of electric vehicles, various power quality problems are encountered mainly during vehicle battery charging. Thus, this research work focuses on power quality improvement in electric vehicle battery charging stations. In this article, a closed-loop battery current-controlled zeta converter with a PI controller is introduced to achieve quality power to charge electric vehicles. The proposed converter enhances the overall performance of the system by reducing voltage fluctuations, harmonic content, and frequency variations. Besides, this suggested closed-loop battery-controlled zeta converter improves the power factor and overall efficiency of the system. The converter provides a wide range of output with ripple-free current. In the proposed scheme, the vehicle battery current feedback to the PI controller generates the switching pulses, thereby generating the desired duty ratio to operate the converter to maintain a constant current. The entire system is implemented in MATLAB/Simulink and various power quality parameters namely voltage and current characteristics, active and reactive power characteristics, frequency, total harmonic distortion (THD), power factor, and efficiency are measured. To validate the usefulness of the proposed scheme, it is compared with conventional buck converter-based charging station and conventional zeta converter-based charging station. From the results, it is found that the proposed closed-loop battery current-controlled zeta converters charging station produce improved power quality characteristics over conventional methods. It achieved a voltage THD of 4.93%, current THD of 1.9%, power factor of 0.96, and efficiency of 91.8%, which are far better than the conventional buck and zeta models.

Economic scheduling and dispatching of distributed generators considering uncertainties in modified 33-bus and modified 69-bus system under different microgrid regions

2024-08-23T10:59:28+00:00

This paper presents a comprehensive framework for the economic scheduling and dispatching of Distributed Generators (DGs) in modified 33-bus and 69-bus systems across multi-microgrid regions. The framework introduces two key techniques: a novel dispatch strategy for optimizing the charging and discharging of Electric Vehicle (EV) batteries, and a robust power dispatch method for islanded distribution systems. The EV dispatch strategy uses a multi-criteria decision analysis method, Probabilistic Elimination and Choice Expressing Reality (p-ELECTRE), to maximize profits for EV owners while meeting power system requirements. This strategy is tested on fleets of 100 and 200 EVs with random travel plans within the modified 33-bus and 69-bus systems, and employs the BAT Optimization Algorithm (BOA) for optimal power dispatch. The second technique addresses the power dispatch in islanded systems by sectionalizing them into self-supplied microgrids, aiming to minimize operational costs, system losses, and voltage deviation using the Jaya algorithm. Additionally, a multi-objective cost-effective emission dispatch is evaluated using Whale Optimization Algorithm (WOA), showing superior performance over Differential Evolution (DE), Particle Swarm Optimization (PSO), and Grey Wolf Optimization (GWO). Comparative analysis highlights the scalability and adaptability of the proposed approach, making it a valuable tool for efficient microgrid management. Simulation results confirm significant improvements in cost savings, system reliability, and operational efficiency under various uncertainty scenarios.

Design of PV fed single-switch transformer less topology powered electric vehicle

2024-05-15T18:33:21+00:00

As a result of an increase in the availability of resources that were not harmful to the environment, solar energy applications shot to popularity. Photovoltaic cells power systems that necessitate DC-DC converters because of their low voltage output. This investigation uses photovoltaic cells (PV) to power a high-voltage gain design with just one switch and no transformer. The proposed circuit utilizes a single regulated switch, which contributes to a reduction in switching losses. It requires fundamental pulse regulation. The network used a switched capacitor cell and an LC passive filter to provide an accurate step-up voltage. We can obtain the equation for the step-up voltage gain from the steady-state continuous conduction mode. The equations used for the theoretical design of converters include energy. To show that the topology is comparable with other modern converters that have been published, a comparison was made between it and other converters. In order to validate the converter's effectiveness, simulations built in MATLAB and Simulink are used.

Risk assessment of electric power generation systems using modified jellyfish search algorithm

2024-05-27T04:00:10+00:00

An electric utility's main goal is to fulfil the requirements and expectations of its customers by providing power. When there are uncertainties, like equipment failures, system reliability evaluation offers a framework to guarantee that the system will still function properly. A modified Jellyfish Search Algorithm (JFSA) has been proposed for estimation of Electric power generation system reliability indices. Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and other modified versions of algorithms have been used in algorithms that use optimization methods for the assessment of reliability indices. Jelly Fish Search Algorithm has been used in power systems to find the economic load dispatch of generating units, for integration of Distributed Generation (DG) units, Maximum Power tracking of PV system and Optimal Power Flow solutions etc. However, JFSA has not been implemented for the evaluation of reliability indices for electric power generation system. In this context a modified JFSA algorithm is developed for evaluation of certain reliability indices such as Loss of Load Expectation (LOLE), and Expected Demand Not Supplied (EDNS), Loss of Load Probability (LOLP). The algorithm presented is implemented on two test system which are RBTS 6 bus system and IEEE 24 bus Reliability Test System. The Results obtained are compared for different models of Generation and Load and are analysed.

Optimization of combustion characteristics on a diesel engine fueled by Mahua biodiesel with dispersion of graphene oxide and zinc oxide nanoparticles as additives using design of experiment

2024-07-04T07:46:17+00:00

The current research investigates the effects of adding metallic graphene oxide (GO) and non-metallic zinc oxide (ZnO) nanoparticles to Mahua biodiesel blend (B20) on the combustion parameters of a diesel engine. GO and ZnO nanoparticles were utilized at a concentration of 75 mg/L, combined with a 1:1 mixture of the surfactant CTAB and the dispersant TWEEN 80. When nanoparticles were introduced to blended biofuel, combustion parameters such as cumulative heart rate, mean gas temperature, mass percent burnt, and rise of pressure increase (RoPR) greatly improved at higher injection pressures. When compared to clean diesel, utilizing B20+ZnO Nanoparticles+ NIS dispersant at 250 bar resulted in 6%, 15%, 7%, and 7.6% improvements in CHRR, MGT, MFB, and RoPR, respectively. The correlation coefficient (R²) for B20+ZnO NPs+ NIS (1:1) for CHRR, MGT, MFB and RoPR is 0.975, 0.978, 0.966 and 0.9883 when compared to GO nanoparticle inclusions, considering it as optimum combination and an efficient fuel. When compared to other fuel samples, the CHRR, MGT, MFB and RoPR for B20+ZnO NPs+ NIS are 2.484%, 3.2%, 2.6% and 1.25% higher, respectively, according to a statistical analysis conducted by design expert.

Impact of high blends of Madhuca Logifolia biodiesel on the performance, combustion and emission parameters in a CRDI diesel engine at variable compression ratio

2024-07-10T09:25:28+00:00

The country today uses a variety of industrial and transportation facilities that are fueled by diesel fuel. However, because of its non-sustainable and polluting nature, there is an urgent need for a more environmentally acceptable substitute that can be utilized in existing engines with no or little modification. Madhucalongifolia (Mahua) was considered a main source for biodiesel production based on its availability and its nature to not impact the food chain. The raw oil was converted to biodiesel using the process of transesterification. The higher blends of B80 (80% mahua biodiesel, 20% diesel by vol.) and B90 (90% mahua biodiesel, 10% diesel by vol.) were prepared. The experiment was carried out using an eddy current dynamometer and involved a Kirloskar 4-stroke single-cylinder which was water-cooled, CRDI diesel engine. The base run was generated using 18:1 compression ratio diesel fuel. These outcomes were contrasted with identical engine conditions using blends of B80 and B90 biodiesel as fuel. The most favourable results in terms of the engine parameters ie. BTE, SFC, cylinder pressure, HC, NOx and CO were as stated here. There was an increase of 8.87% in BTE for the B90 blend. A minor increase of 2.77% in SFC was observed with the B90 blend. The cylinder pressure for B90 was decreased by 0.024%. The emissions for B80 and diesel were lesser in comparison to B90. Diesel showed the lowest CO (7.9%) emissions whereas HC and NOx for B80 decreased by 24.39% and 3.42% respectively. The engine was made to run at two lower compression ratios of 16 and 17. When using a fuel blend of B80 at a compression ratio of 16, the performance metrics were significantly better. It could be concluded that, the compatible results were found with B80 biodiesel blend with compression ratio of 16. The BTE, SFC, cylinder pressure, HC, NOx and CO were quantified as 25.61%, 0.34kg/kWh, 30.27 bar, 50ppm, 1204 ppm and 0.24% by volume respectively. In comparison to the base run (diesel fuel and compression ratio of 18), there was 15.98% increase in the BTE, 5.55% decrease in the SFC, 16.07% decrease in the cylinder pressure, 21.95% decrease in the emission of HC, 23.55% decrease in NOx and 9.09% increase in CO emissions.

IOT Based Intelligent Energy Monitoring of Grid connected Hybrid systems and controlling of Loads using PLC

2024-07-03T15:29:29+00:00

Nowadays IOT becoming popularize in all the application especially in the power system network for data monitoring from the Hybrid power distribution system. Because of easy adaptability of IOT technology, it find its place in data monitoring for the remote system and data can also be logged in the cloud server for analysis of the system under surveillance. By having data enabled IOT system, which will make the complete system smarter in terms of monitoring and analysis of the performance of the power distribution network. In these research paper, concept IOT technology for data monitoring of grid connected Hybrid system consist of PV source for the typical educational institute was developed and implemented in the campus. Apart from the data monitoring, controlling of the critical loads connected to this hybrid system was developed using Programmable Logic Controller (PLC). The MyQtt based cloud server was used to store the data pushed from the IOT device and user interactive mobile Application was developed using MIT inventor to monitor the data in the mobile itself, the command from the Mobile app was given to the PLC to control the loads. The energy data from the Multi-function energy meter (MFM) is pushed to PLC through gateway of Raspberry Pi. In this paper, Raspberry PI was used as IOT device and ILC 131 ETH PLC was used to control the loads. Performance of IOT device along with PLC was monitored for 3 months and results obtained were satisfactory.

Evaluation of wind energy-based electricity potential at 40m height in 3 districts of Bihar

2024-06-11T00:50:26+00:00

In this study, three northern districts of Bihar, namely, Bettiah, Madhubani, and Muzaffarpur, were carefully chosen to understand the wind energy resource availability for energy conversion. The wind speed data set was gathered from local meteorological station for a period of 12 years from April 2008 to March 2020. For evaluating wind resources, the widely used two-parameter Weibull distribution was combined with other statistical techniques. The obtained results exhibited that the wind speed in the three districts of Bihar fluctuated from 3.2 m/s to 4.7 m/s while the scale and shape parameters (also known as the Weibull parameters) are discovered to be in the ranges of 10.247 to 11.432 and 3.5718 to 4.0477, respectively. Further, wind power density was 22.729, 26.288, and 28.021 W/m$^2$ at 40 m above the ground for the three districts considered in the study. These findings suggested that the places of the study are not suitable for widespread wind power extraction at a hub height of 40 m but suitable for small-scale wind turbine setup.

Extraction of 4-HBA utilizing renewable and conventional solvents

2024-09-20T11:10:06+00:00

The valuable chemical 4-hydroxybenzoic acid (4-HBA), the phenolic compound of carboxylic acid possesses promise as an antioxidant, antifungal, anticancer, antidiabetic, and cardioprotective properties. 4-HBA has promising uses in the pharmaceutical, cosmetic, and plastic sectors, making it worthwhile to recover. Using renewable solvent like Karanja oil and traditional solvents like n-Butanol, and Di-chlorobenzene, the experimental tests were carried out to separate 4-hydroxybenzoic acid from solute mixed aqueous solution. Parameters including the distribution coefficient K_D, the percentage extraction efficiency %E, the partition coefficient P, and the dimerization constant D were discovered and associated with several solvent physicochemical features while evaluating the equilibrium for this physical extraction experiment. The following parametric values were obtained for K_D and %E: n-Butanol (1.704, 63.02%)> Karanja oil (0.810, 44.75%) > Di-chlorobenzene (0.555, 35.69%).

Implementation plan of the information security management system based on the NTC-ISO-IEC 27001:2013 standard and security risk analysis. Case study: Higher education institution

2024-06-15T12:11:14+00:00

This research was carried out to generate an implementation plan for the information security management system based on the NTC-ISO-IEC 27001:2013 standard and security risk analysis at the IUB university institution. The connotation of security has been extended over time due to technological advances and the introduction of new information systems, which simultaneously generate new security challenges. Likewise, the instruments to guarantee the confidentiality, integrity, and availability of information have become a fundamental strategy to ensure the security of public and private organizations. The preparation of this plan includes the methodological cycle, where they indicate a series of phases and their corresponding activities to implement the ISMS ISO 27001:2013, with procedural characteristics that support the entire implementation process from beginning to end, facilitating due process and continuity. Likewise, an analysis of the Information security risk plan is carried out, of which there is significant progress. The result of this cycle will be a plan with a schedule of activities so that the organization links all the personnel around compliance with the standard, raising awareness regarding the importance of information security and the development of activities in phases that, within the stipulated times, will be able to have the ISMS fully operational

Multi-objective grounding system optimisation using NSGA-II

2024-05-14T12:38:37+00:00

This study investigates the optimisation of grounding infrastructure in substations by implementing the philosophy of the multi-objective algorithm NSGA-II Elite. A complete description of the operating scheme and the characteristic mechanisms that support the behaviour and development of optimal Pareto solutions is provided. A detailed comparison was made with the optimisation method used in the GMAT program of Aplicaciones Tecnológicas, based on a semi-optimization process derived from the correlation of semi-precision optimisation solutions. The results show that multi-objective optimisation using NSGA-II results in a significant cost reduction compared to the semi-optimization method, although the computational time required to reach the final solution increases significantly. This approach allows a more adequate understanding of optimising the terrestrial substation grid. It highlights its ability to generate more cost-effective and performance-efficient solutions by carefully considering the computing time required.

A battery energy storage system as an alternative for mitigating issues in the distribution network

2024-01-27T14:56:13+00:00

distribution network to boost energy efficacy. The investigation specifically scrutinizes the integration of BESS into the distribution network feeder, both in the presence and absence of distributed photovoltaic penetration, with a focus on enhancing load factor and power factor. Utilizing the OpenDSS software with a Python interface, simulations were conducted to assess a range of scenarios involving the injection and absorption of active and reactive power. The outcomes underscore noteworthy enhancements in both load factor and power factor at the feeder output. Additionally, the integration of BESS exhibits a reduction in power losses along the feeder. These findings offer valuable perspectives for advancing energy efficiency in distribution networks, and have implications for future research and practical implementation.

Automatic differential kinematics of serial manipulator robots through dual numbers

2024-05-12T16:40:40+00:00

Dual Numbers are an extension of real numbers known for its capability of performing exact automatic differentiation of one-valued functions theoretically without error approximation. Also, Differential Kinematics of robots involves the computation of the Jacobian, which is a matrix of partial derivatives of the Forward Kinematic equations with respect to the robot’s joints. Thus, to perform the automatic calculation of the Jacobian matrix, this paper presents an extension of dual numbers composed of a scalar real part and a vector dual part, where the real part represents the angular value of the robot joint, and the dual part represents the direction of the corresponding partial derivative for each joint. The presented method was implemented in Matlab through Object Orientes Programming (OOP), and the results for calculating the Jacobian of the KUKA KR 500 robot model for 1000 random postures were subsequently compared in terms of execution time and Mean Squared Error (MSE) with other conventional methods: the geometric method, the symbolic method, and the finite difference method. The results showed a significant improvement in the computing time for calculating the Jacobian of the robotic model compared to the other methods, as well as a minimum MSE having as reference the numerical value of the symbolic calculations.

Enhancing disaster management through multi-objective water wave optimization for medical supplies storage and distribution

2024-05-14T12:55:02+00:00

This paper conducts a comparative analysis of advanced methodologies aimed at addressing the intricate task of scheduling medical supplies in both civilian and military sectors for epidemic prevention and control. This study introduces a multi-objective water wave optimization (MOWWO) algorithm and enhance its efficacy by incorporating a dynamically adjusted component to the metaheuristic approach (DAMOWWO). The primary goal of this research is to assess the proposed approach in contrast to established state of the art methods with similar objectives. The aim of this study is to optimize multiple aspects simultaneously, including the overall satisfaction rates of medical supply delivery and the reduction of scheduling costs, while ensuring a minimum military supply reservation ratio. This paper offers a comprehensive evaluation of the MOOW algorithm, emphasizing its potential applications in emergency response scenarios.

A Computer vision based system for human detection and automatic people counting

2024-10-25T16:47:19+00:00

Occupancy control is a fundamental aspect of managing spaces and services effectively. It aims to ensure safety, compliance with regulations, emergency preparedness, and overall satisfaction for individuals and businesses. To align with the described need, this paper presents a computer vision based system for automatic people counting in gates. The system is divided in five stages: video capture, motion analysis, human detection, human tracking and people counting. An RGB camera captures the top-view image of the gate and analyze the change or movement in the objects in scene. When motion is detected, the frame is sent to the object detector, which is a convolutional neural network. Then, a tracking algorithm analyzes the movement patterns of people. According to the route, it is determined whether the person arrives or leaves and the count is updated. Two test scenarios are analyzed: the entry of a public bus and a building gate. The people detection module is tested, showing a mAP of 95.2% and a mean IoU (50%) of 55.9%. Also, the counting is tested showing an average precision of 96.8%, a recall of 92% and an F1-Score of 94.3%. Finally, the system performance is evaluated, showing an average processing time of 34.2 ms and 29.2 FPS.

UniSchedApi: A comprehensive solution for university resource scheduling and methodology comparison

2024-10-26T20:08:55+00:00

This paper introduces UniSchedApi, an API-based solution that revolutionizes optimized university resource scheduling. The primary focus of the research is the detailed evaluation of two automatic resource allocation methods: Tabu Search (TS) and Genetic Algorithm (GA). The paper thoroughly explores how these methods address challenges associated with resource allocation in university environments, considering critical factors such as teacher availability, student time constraints, classroom features (including computers, projectors, TV's, specialized laboratories, specialized equipment, etc.), among others. The evaluation is carried out meticulously, measuring the performance and memory resource usage of both algorithms, considering the comparison with the manual scheduling. The results reveal that the TS algorithm excels in terms of temporal efficiency and computational resource usage. Based on these findings, UniSchedApi implements GA and TS but uses TS as the default algorithm, ensuring more efficient and optimized management of academic resources. This research not only presents a practical solution with UniSchedApi but also provides a deep understanding of the methods for evaluating and selecting algorithms to address specific challenges in university resource allocation. These results lay the groundwork for future improvements in academic resource management.

Technological prototype with artificial intelligence for military security in river environments

2024-11-06T21:35:51+00:00

Maritime and river security is one of the main concerns of military forces due to the large number of illicit activities that occur. Not to mention the extensive areas that must be monitored, and the weather conditions that can occur. Currently, technologies have become fundamental to leave aside manual surveillance for intelligent systems that allow remote sensing, traffic control, and object detection. Based on the aforementioned problems, the purpose of this research was to design a technological prototype with artificial vision based on an artificial intelligence model to detect water vessels and people in river environments as a support tool for military security. The prototype used at hardware level a Raspberry Pi 3 card and four pre-trained models based on R-CNN, YOLO, EfficientDet and SSD (single shot multibox). The best-performing model was the Mobilnet V2 SSD, with an mAP of 0.83 and an FPS of 5. Finally, this tool can contribute to strengthening the strategic, tactical, and operational capabilities of actors in the military intelligence sector, aimed at protecting sovereignty and territorial integrity to establish an environment of security in society.

Evaluating generative AI tools: A comparative guide for new users

2024-11-06T21:53:51+00:00

One of the most prominent forms of artificial intelligence that has been made public is generative AI, which offers a fresh perspective and direction for numerous global sectors. The impact of this artificial intelligence model on nearly every business and how certain vocations can be pursued has garnered a large audience. This paper evaluates generative AI from an objective standpoint and proposes a comparison of some of its key tools to help new users solve problems in their daily lives, at work or at home. This highlights the significance of understanding and utilizing this kind of AI.

Academic research contributions from Ibero-American countries to general knowledge, engineering, and computer science up to 2023 and COVID impact

2024-11-13T12:57:26+00:00

Although commonly, competitive intelligence analysis is applied to companies, where new investments and operation expenditures are considered along with changes in the market, competitive intelligence can be applied to different countries' academic research production, which impacts science, technology and innovation making a difference in their competitiveness. This work shows the academic research production for all areas of knowledge, and also specifically for engineering and computer science, from all the Ibero-American countries, based on the SCOPUS database from 2001 to 2023. The results show that based on the volume of their academic production, for an affordable analysis, the Ibero-American countries can be classified into three groups, considering an order of magnitude difference for each group. The leading group is composed of countries producing between 200,000 and about 2 million documents, the second group of countries producing between 20,000 and 200,000 documents, and the third group of countries producing between 2,000 and 20,000 documents. The COVID pandemic impacted all the countries which showed a decrease in annual production over the last two years. The results also show the priority areas of knowledge that these countries invest in and the main countries that they collaborate with. The behavior of production for engineering and computer science is like that of general production, but there are some specific internal factors for specific countries such as in the case of Brazil, the country in which production fell most dramatically, followed by Spain and Colombia. On the other hand, Peru and Ecuador show a high interest in engineering and computer science. This work offers a high value decision-making tool to leaders in academic research, the principals of research driven institutions and investors from industry in Ibero-America. The academic production is related to the size of the GDP and the percentage of the GDP invested by a country in research development and innovation.

Exploring the determinants of happiness in Mexico: The interplay of social networks, psychological well-being, and socioeconomic factors

2024-12-03T15:02:06+00:00

Over recent years, there has been significant growth in research on happiness. It is essential to understand the factors that affect people's well-being to develop effective government policies that aim to improve the quality of life for citizens in Mexico. Unfortunately, this subject has been under-explored, especially in the Mexican context, with limited studies focusing on the topic. This study aims to comprehensively review the current literature on happiness, social networking, psychological factors, and socioeconomic factors to identify the critical variables associated with the happiness of Mexican citizens. Further, based on data from Mexico's 2021 National Survey of Self-reported Well-being (ENBIARE), we conducted a rigorous examination of this dataset to identify the principal factors that impact the well-being of Mexican citizens, employing both exploratory and confirmatory factor analysis.

A Comprehensive Method for Designing Containerized Microgids in Non-Interconnected Zones

2024-12-03T15:19:25+00:00

Many isolated rural communities lack basic electricity services and associated modern amenities. One proposed solution is the deployment of containerized microgrids, which are clusters of generation and storage assets packaged in a container for easy deployment. However, few works have described approaches for designing such solutions. This paper presents a five-step method for designing a containerized photovoltaic-based microgrid for isolated areas. The method includes defining system design requirements and constraints (technical, environmental, and legal), conducting preliminary studies on solar radiation and load profiles, selecting equipment, designing the control system, and performing a basic economic analysis. This method is verified in three scenarios of Colombian Non-Interconnected Zones (NIZs), resulting in a solution that can effectively provide electricity to the isolated communities, primarily from solar energy, store surplus generation in batteries, and minimize diesel backup generator use. The results show that the solutions can be scaled to feed larger loads and can be applied in other contexts, such as emergency supply after natural disasters.

Classification of opening/closing hand motor imagery induced by left and right robotic gloves through EEG signals

2024-12-18T15:52:57+00:00

This study presents a novel strategy for classifying Motor Imagery (MI) related to hand opening/closing actions using electroencephalography signals. This approach combines the passive motion induced by a robotic glove and action observation. Two groups of subjects executed a protocol based on left and right hand movement MI to address this. Subsequently, spectral features were used on $mu$ and $beta$ bands, and machine-learning algorithms were used for classification. The results showed better performance for right-hand motion recognition using k-Nearest Neighbors (kNN), which achieved the highest performance metrics of 0.71, 0.76, and 0.28 for Accuracy (ACC), true positive rate, and false positive rate, respectively. These findings demonstrate the feasibility of the proposed methodology for improving the recognition of MI tasks of the same limb, which can contribute to the design of more robust brain-computer interfaces for the enhancement of rehabilitation therapy for post-stroke patients.